Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions
- Author
- Nasrulloh Ratu Bagus Satrio Loka (UGent) , Ivo Couckuyt (UGent) , Federico Garbuglia (UGent) , Domenico Spina (UGent) , Inneke Van Nieuwenhuyse and Tom Dhaene (UGent)
- Organization
- Project
- Abstract
- Multi-objective optimization of complex engineering systems is a challenging problem. The design goals can exhibit dynamic and nonlinear behaviour with respect to the system's parameters. Additionally, modern engineering is driven by simulation-based design which can be computationally expensive due to the complexity of the system under study. Bayesian optimization (BO) is a popular technique to tackle this kind of problem. In multi-objective BO, a data-driven surrogate model is created for each design objective. However, not all of the objectives may be expensive to compute. We develop an approach that can deal with a mix of expensive and cheap-to-evaluate objective functions. As a result, the proposed technique offers lower complexity than standard multi-objective BO methods and performs significantly better when the cheap objective function is difficult to approximate. In particular, we extend the popular hypervolume-based Expected Improvement (EI) and Probability of Improvement (POI) in bi-objective settings. The proposed methods are validated on multiple benchmark functions and two real-world engineering design optimization problems, showing that it performs better than its non-cheap counterparts. Furthermore, it performs competitively or better compared to other optimization methods.
- Keywords
- MULTIOBJECTIVE OPTIMIZATION, EXPECTED IMPROVEMENT, ALGORITHM, DESIGN, PAREGO, MODEL, Multi-objective optimization, Bayesian optimization, Hypervolume, Gaussian process
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8738882
- MLA
- Satrio Loka, Nasrulloh Ratu Bagus, et al. “Bi-Objective Bayesian Optimization of Engineering Problems with Cheap and Expensive Cost Functions.” ENGINEERING WITH COMPUTERS, vol. 39, no. 3, 2023, pp. 1923–33, doi:10.1007/s00366-021-01573-7.
- APA
- Satrio Loka, N. R. B., Couckuyt, I., Garbuglia, F., Spina, D., Van Nieuwenhuyse, I., & Dhaene, T. (2023). Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions. ENGINEERING WITH COMPUTERS, 39(3), 1923–1933. https://doi.org/10.1007/s00366-021-01573-7
- Chicago author-date
- Satrio Loka, Nasrulloh Ratu Bagus, Ivo Couckuyt, Federico Garbuglia, Domenico Spina, Inneke Van Nieuwenhuyse, and Tom Dhaene. 2023. “Bi-Objective Bayesian Optimization of Engineering Problems with Cheap and Expensive Cost Functions.” ENGINEERING WITH COMPUTERS 39 (3): 1923–33. https://doi.org/10.1007/s00366-021-01573-7.
- Chicago author-date (all authors)
- Satrio Loka, Nasrulloh Ratu Bagus, Ivo Couckuyt, Federico Garbuglia, Domenico Spina, Inneke Van Nieuwenhuyse, and Tom Dhaene. 2023. “Bi-Objective Bayesian Optimization of Engineering Problems with Cheap and Expensive Cost Functions.” ENGINEERING WITH COMPUTERS 39 (3): 1923–1933. doi:10.1007/s00366-021-01573-7.
- Vancouver
- 1.Satrio Loka NRB, Couckuyt I, Garbuglia F, Spina D, Van Nieuwenhuyse I, Dhaene T. Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions. ENGINEERING WITH COMPUTERS. 2023;39(3):1923–33.
- IEEE
- [1]N. R. B. Satrio Loka, I. Couckuyt, F. Garbuglia, D. Spina, I. Van Nieuwenhuyse, and T. Dhaene, “Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions,” ENGINEERING WITH COMPUTERS, vol. 39, no. 3, pp. 1923–1933, 2023.
@article{8738882,
abstract = {{Multi-objective optimization of complex engineering systems is a challenging problem. The design goals can exhibit dynamic and nonlinear behaviour with respect to the system's parameters. Additionally, modern engineering is driven by simulation-based design which can be computationally expensive due to the complexity of the system under study. Bayesian optimization (BO) is a popular technique to tackle this kind of problem. In multi-objective BO, a data-driven surrogate model is created for each design objective. However, not all of the objectives may be expensive to compute. We develop an approach that can deal with a mix of expensive and cheap-to-evaluate objective functions. As a result, the proposed technique offers lower complexity than standard multi-objective BO methods and performs significantly better when the cheap objective function is difficult to approximate. In particular, we extend the popular hypervolume-based Expected Improvement (EI) and Probability of Improvement (POI) in bi-objective settings. The proposed methods are validated on multiple benchmark functions and two real-world engineering design optimization problems, showing that it performs better than its non-cheap counterparts. Furthermore, it performs competitively or better compared to other optimization methods.}},
author = {{Satrio Loka, Nasrulloh Ratu Bagus and Couckuyt, Ivo and Garbuglia, Federico and Spina, Domenico and Van Nieuwenhuyse, Inneke and Dhaene, Tom}},
issn = {{0177-0667}},
journal = {{ENGINEERING WITH COMPUTERS}},
keywords = {{MULTIOBJECTIVE OPTIMIZATION,EXPECTED IMPROVEMENT,ALGORITHM,DESIGN,PAREGO,MODEL,Multi-objective optimization,Bayesian optimization,Hypervolume,Gaussian process}},
language = {{eng}},
number = {{3}},
pages = {{1923--1933}},
title = {{Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions}},
url = {{http://doi.org/10.1007/s00366-021-01573-7}},
volume = {{39}},
year = {{2023}},
}
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